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Create the Django app or the virtual environment first?

I have been trying to successfully create projects using Django however I have seen projects where the user will create the project first THEN the virtual env. I have also seen instances where the user creates the virtual env and THEN the django app. Both sides argue that their method is better, but now I am confused. Pls help

it is better to create the virtual environment first and start working in that environment. ie use python from that environment.
advantage:
a. environment will contain all the package required by the project
b. can switch between multiple env( testing purpose)
c. easy to keep a record of the required packages
d. will not affect another project where u need python 3.5 and in django project u require python 3.6
disadvantage:
need to keep track of each env in case if you have many virtual env ( all virtual env are store in same place just like anaconda one, else if store in project folder then no issue for1 env)

It depends on your usage.
Let's say you have Django 2.1 installed globally, then you have a project where you need let's say Django 1.9, here you need to set-up your virtual environment first

When you have a virtual environment you can track packages for each project.
When virtual environment is activated you can create requirements.txt file with command
pip freeze > requirements.txt
So when you want to run the django project to a different os you can install your packages from the requirements file you have created.
pip install -r requirements.txt
An other scenario is when your os has django 1.11 and you have a django project created with that version. When you upgrade the django version in your os the the django application will break.
So i think that for each django project a good way is to have its own virtual environment

Python project folder and its Miniconda2 environment folder

I am a beginner trying to learn a bit of Python; first practical applications will be data analytics. My learning setup consists of Mac OS X, Miniconda2, Pycharm and Git.
Is it better to set up a project folder 'bar' within a conda environment folder 'foo' (~/miniconda2/env/foo/bar)?
Or is it better to leave the conda environment alone as ~/miniconda2/env/foo and set up a project folder as ~/repos/bar?
Virtualenv users I've seen put the env and the project in a single folder, but I have not seen a similar, popular or recommended workflow for conda.
Thank you in advance for any advice.

While I haven't used conda myself, I expect they aren't trying to change the concept of a virtual environment too much. That being said, I personally find it better to keep them separate, i.e. have a ~/.virtualenvs and a ~/repos folder.
As you mentioned, though, it's pretty common to store both the virtualenv and the project itself in the same folder. What I would stress here is that the virtualenv should then be in the project folder, not the other way around. For example:
~/repos/Foo/.fooenv
The reason for this is that virtualenvs should be disposable, whereas your projects are not. That means that you should be able to freely remove a virtualenv without fearing you've accidentally deleted your project folder along with it.

Can I have multiple django projects in the same virtualenv folder

Another newbie to django here. I was wondering if it is recommended/not-recommended to run two different projects in the same virtualenv folder that have the same django version. To be more clear, is it necessary to create separate virtualenv everytime I want to start a new project when i know that i am using same django version for all projects. I am using python django on OSX.

It really depends on the situation. Suppose if your Project A need to use Pip version 17.01 to run while your project B need to use Pip version 18.01 to run. So It is not possible to use 1 virtual env to run multiple project but the downside of having multiple virtual environments is it consume much space & resource of PC.

Why is virtualenv necessary?

I am a beginner in Python.
I read virtualenv is preferred during Python project development.
I couldn't understand this point at all. Why is virtualenv preferred?

Virtualenv keeps your Python packages in a virtual environment localized to your project, instead of forcing you to install your packages system-wide.
There are a number of benefits to this,
the first and principle one is that you can have multiple virtulenvs, so you
can have multiple sets of packages that for different projects, even
if those sets of packages would normally conflict with one another.
For instance, if one project you are working on runs on Django 1.4
and another runs on Django 1.6, virtualenvs can keep those projects
fully separate so you can satisfy both requirements at once.
the second, make it easy for you to release your project with its own dependent
modules.Thus you can make it easy to create your requirements.txt
file.
the third, is that it allows you to switch to another installed python interpreter for that project*. Very useful (Think old 2.x scripts), but sadly not available in the now built-in venv.
Note that virtualenv is about "virtual environments" but is not the same as "virtualization" or "virtual machines" (this is confusing to some). For instance, VMWare is totally different from virtualenv.

A Virtual Environment, put simply, is an isolated working copy of Python which allows you to work on a specific project without worry of affecting other projects.
For example, you can work on a project which requires Django 1.3 while also maintaining a project which requires Django 1.0.

VirtualEnv helps you create a Local Environment(not System wide) Specific to the Project you are working upon.
Hence, As you start working on Multiple projects, your projects would have different Dependencies (e.g different Django versions) hence you would need a different virtual Environment for each Project. VirtualEnv does this for you.
As, you are using VirtualEnv.. Try VirtualEnvWrapper : https://pypi.python.org/pypi/virtualenvwrapper
It provides some utilities to create switch and remove virtualenvs easily, e.g:
mkvirtualenv <name>: To create a new Virtualenv
workon <name> : To use a specified virtualenv
and some others

Suppose you are working on multiple projects, one project requires certain version of python and other project requires some other version. In case you are not working on virtual environment both projects will access the same version installed in your machine locally which in case can give error for one.
While in case of virtual environment you are creating a new instance of your machine where you can store all the libraries, versions separately. Each time you can create a new virtual environment and work on it as a new one.

There is no real point to them in 2022, they are a mechanism to accomplish what C#, Java, Node, and many other ecosystems have done for years without virtual environments.
Projects need to be able to specify their package and interpreter dependencies in isolation from other projects. Virtual Environments are a fine but legacy solution to that issue. (Vs a config file which specifies interpreter version and local __pypackages__)
pep 582 aims to address this lack of functionality in the python ecosystem.

Developing and using the same Python on the same computer

I'm developing a Python utility module to help with file downloads, archives, etc. I have a project set up in a virtual environment along with my unit tests. When I want to use this module on the same computer (essentially as "Production"), I move the files to the mymodule directory in the ~/dev/modules/mymodule
I keep all 3rd-party modules under ~/dev/modules/contrib. This contrib path is on my PYTHONPATH, but mymodule is NOT because I've noticed that if mymodule is on my PYTHONPATH, my unit tests cannot distinguish between the "Development" version and the "Production" version. But now if I want to use this common utility module, I have to manually add it to the PYTHONPATH.
This works, but I'm sure there's a better, more automated way.
What is the best way to have a Development and Production module on the same computer? For instance, is there a way to set PYTHONPATH dynamically?

You can add/modify python paths at sys.path, just make sure that the first path is the current directory ".", because some third-party modules rely on importing from the directory of the current module.
More information on python paths:
http://djangotricks.blogspot.com/2008/09/note-on-python-paths.html

I'm guessing by virtual environment you mean the virtualenv package?
http://pypi.python.org/pypi/virtualenv
What I'd try (and apologies if I've not understood the question right) is:
Keep the source somewhere that isn't referenced by PYTHONPATH (e.g. ~/projects/myproject)
Write a simple setuptools or distutils script for installing it (see Python distutils - does anyone know how to use it?)
Use the virtualenv package to create a dev virtual environment with the --no-site-packages option - this way your "dev" version won't see any packages installed in the default python installation.
(Also make sure your PYTHONPATH doesn't have any of your source directories)
Then, for testing:
Activate dev virtual environment
Run install script, (usually something like python setup.py build install). Your package ends up in /path/to/dev_virtualenv/lib/python2.x/site-packages/
Test, break, fix, repeat
And, for production:
Make sure dev virtualenv isn't activated
Run install script
All good to go, the "dev" version is hidden away in a virtual environment that production can't see...
...And there's no (direct) messing around with PYTHONPATH
That said, I write this with the confidence of someone who's not actually tried setting using virtualenv in anger and the hope I've vaguely understood your question... ;)

You could set the PYTHONPATH as a global environment variable pointing to your Production code, and then in any shell in which you want to use the Development code, change the PYTHONPATH to point to that code.
(Is that too simplistic? Have I missed something?)